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Bioimage-based protein subcellular location prediction： a comprehensive review

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Frontiers of Computer Science 2018年第1期12卷 26-39页

作者： Ying-Ying XU Li-Xiu YAO hong-bin shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location pat- terns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic char- acteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systemati- cally reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and clas- sified them into four categories including growing of bioim- age databases, description of subcellular location distribution patterns, classification methods, and applications of the pre- diction systems. Besides, we also discussed some potential directions in this field.

关键词： bioimage informatics protein subcellular loca-tion prediction global and local features multi-location pro-tein recognition

Identifying RNA-binding proteins using multi-label deep learning

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Science China(Information Sciences) 2019年第1期62卷 217-219页

作者： Xiaoyong PAN Yong-Xian FAN Jue JIA hong-bin shen Department of Medical Informatics Erasmus MC Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Affiliated Hospital of Jiangsu University Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

Dear editor,RNA-binding proteins (RBPs) are involved in both transcriptional and post-transcriptional gene regulation, such as RNA splicing and localization. In addition, their dysregulations are closely associated with many diseases [1]. For example, mutations in the RBPs FUS and TDP-43that can cause amyotrophic lateral sclerosis [2].

关键词： RBP RNA Identifying RNA-binding proteins using multi-label deep learning

Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

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Nano-Micro Letters 2018年第1期10卷 12-19页

作者： Xi Yin Jing Yang Feng Xiao Yang Yang hong-bin shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Computer Science Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.

关键词： Transmembrane a-helices Structure prediction Machine learning Contact map prediction Relative accessible surface area

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OUGENE： a disease associated over-expressed and under-expressed gene database

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Science Bulletin 2016年第10期61卷 752-754页

作者： Xiaoyong Pan hong-bin shen Section for Animal Genetics Bioinformatics and Breeding Center for Non-Coding RNA in Technology and Health University of Copenhagen 1870 Frederiksberg Denmark Department of Disease Systems Biology Novo NordiskFoundation Center for Protein Research University ofCopenhagen 1870 Frederiksberg Denmark Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Gene over-expression or under-expression is closely associated with human diseases, which contributes to phenotypic variations and diversity. To our best knowledge, there is no single open specific resource available to provide the association information between gene over- or under-expression and various diseases. In this study, we presented a comprehensive disease-associated over- and under-expressed gene database （OUGene） based on our proposed text mining pipeline and several open curated databases. It contains total 41,269 unique associa- tions between 7,238 over- or under-expressed genes and 1,480 diseases, which are supported by 81,974 evidence sentences from 56,442 articles. The OUGene is compre- hensive and covers most important therapeutic areas. Meanwhile a new scoring system is designed to rank the associations based on benchmarking against hand-curated data. OUGene provides an easy-of-use web interface for researchers to analyze these data and visualize the associ- ated networks, which can give insights to the complex relationships between over- and under-expressed genes and diseases at a system level. It is available at ***. ***/bioinf/OUGene/.

关键词： Text mining Gene over- and under-expression Disease Database

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REVIEW : Recent advances in developing web-servers for predicting protein attributes

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Natural Science 2009年第2期1卷 63-92页

作者： Kuo-Chen Chou hong-bin shen 不详

Recent advance in large-scale genome se-quencing has generated a huge volume of pro-tein sequences. In order to timely utilize the in-formation hidden in these newly discovered sequences, it is highly desired to develop com- putational methods for efficiently identifying their various attributes because the information thus obtained will be very useful for both basic research and drug development. Particularly, it would be even more useful and welcome if a user-friendly web-server could be provided for each of these methods. In this minireview, a sy- stematic introduction is presented to highlight the development of these web-servers by our group during the last three years.

关键词： Cell-PLoc Signal-CF Signal-3L MemType-2L EzyPred HIVcleave GPCR-CA ProtIdent QuatIdent FoldRate

Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms

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Natural Science 2010年第10期2卷 1090-1103页

作者： Kuo-Chen Chou hong-bin shen Gordon Life Science Institute Institute of Image Processing & Pattern Recognition Shanghai Jiaotong Universityn

Cell-PLoc 2.0 is a package of web-servers evolved from Cell-PLoc (Chou, K.C. & shen, H.B., Nature Protocols, 2008, 2:153-162) by a top-down approach to improve the power for predicting subcellular localization of proteins in various organisms. It contains six predictors: Euk-mPLoc 2.0, Hum-mPLoc 2.0, Plant-mPLoc, Gpos-mPLoc, Gneg-mPLoc, and Virus-mPLoc, specialized for eukaryotic, human, plant, Gram- positive bacterial, Gram-negative bacterial, and virus proteins, respectively. Compared with Cell-PLoc, the predictors in the Cell-PLoc 2.0 have the following advantageous features: (1) they all have the capacity to deal with the multiplex proteins that can simultaneiously exist, or move between, two or more subcellular location sites;(2) no accession number is needed for the input of a query protein even if using the “high- level” GO (gene ontology) prediction engine;(3) the functional domain information and sequential evolution information are fused into the “ab initio” sequence-based prediction engine to enhance its accuracy. In this protocol, a step- to-step guide is provided for how to use the web server predictors in the Cell-PLoc 2.0 package, which is freely accessible to the public at http://***/bioinf/Cell-PLoc-2/.

关键词： Euk-mPLoc 2.0 Hum-mPLoc 2.0 Plant-mPLoc Gpos-mPLoc Gneg-mPLoc Virus-mPLoc Higher-level GO approach Ab-initio approach Functional domain Sequential evolution

Prediction of protein folding rates from primary sequence by fusing multiple sequential features

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Journal of Biomedical Science and Engineering 2009年第3期2卷 136-143页

作者： hong-bin shen Jiang-Ning Song Kuo-Chen Chou Bioinformatics Center Institute for Chemical Research Kyoto University Gokasho Uji Kyoto 611-0011 Japan Gordon Life Science Institute 13784 Torrey Del Mar Drive San Diego California 92130 USA. Institute of Image Processing & Pattern Recognition Shanghai Jiaotong University 800 Dongchuan Road Shanghai 200240 China

We have developed a web-server for predicting the folding rate of a protein based on its amino acid sequence information alone. The web- server is called Pred-PFR (Predicting Protein Folding Rate). Pred-PFR is featured by fusing multiple individual predictors, each of which is established based on one special feature derived from the protein sequence. The ensemble pre-dictor thus formed is superior to the individual ones, as demonstrated by achieving higher correlation coefficient and lower root mean square deviation between the predicted and observed results when examined by the jack-knife cross-validation on a benchmark dataset constructed recently. As a user-friendly web- server, Pred-PFR is freely accessible to the public at ***/bioinf/Folding Rate/.

关键词： Protein Folding Rate Ensemble Predictor Fusion Approach Web-Server Pred-PFR

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直线送料器的性能分析及优化

系统仿真学报 2008年第20期20卷 5538-5542,5547页

作者：臧红彬沈沈沈李晓光中国科学技术大学精密机械与精密仪器系合肥230027

针对生产中直线送料器存在的工件迟滞现象、传输效率低等问题,利用机械系统动力学自动分析软件ADAMS建立了系统的虚拟样机模型,基于虚拟样机技术,研究了系统结构和外部激励对其性能的影响,明确了缺陷产生的原因在于激励与系统的失谐,进... 详细信息

针对生产中直线送料器存在的工件迟滞现象、传输效率低等问题,利用机械系统动力学自动分析软件ADAMS建立了系统的虚拟样机模型,基于虚拟样机技术,研究了系统结构和外部激励对其性能的影响,明确了缺陷产生的原因在于激励与系统的失谐,进而对系统的主要构件:板簧、支架、驱动块和送料轨道对系统固有频率和模态的影响进行了深入研究,并据此对系统送料性能提出了优化对策.

关键词：直线送料器虚拟样机动力学分析优化

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Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms

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